Does the use of prediction equations to correct self-reported height and weight improve obesity prevalence estimates? A pooled cross-sectional analysis of Health Survey for England data
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ..
OBJECTIVE: Adults typically overestimate height and underestimate weight compared with directly measured values, and such misreporting varies by sociodemographic and health-related factors. Using self-reported and interviewer-measured height and weight, collected from the same participants, we aimed to develop a set of prediction equations to correct bias in self-reported height and weight and assess whether this adjustment improved the accuracy of obesity prevalence estimates relative to those based only on self-report.
DESIGN: Population-based cross-sectional study.
PARTICIPANTS: 38 940 participants aged 16+ (Health Survey for England 2011-2016) with non-missing self-reported and interviewer-measured height and weight.
MAIN OUTCOME MEASURES: Comparisons between self-reported, interviewer-measured (gold standard) and corrected (based on prediction equations) body mass index (BMI: kg/m2) including (1) difference between means and obesity prevalence and (2) measures of agreement for BMI classification.
RESULTS: On average, men overestimated height more than women (1.6 cm and 1.0 cm, respectively; p<0.001), while women underestimated weight more than men (2.1 kg and 1.5 kg, respectively; p<0.001). Underestimation of BMI was slightly larger for women than for men (1.1 kg/m2 and 1.0 kg/m2, respectively; p<0.001). Obesity prevalence based on BMI from self-report was 6.8 and 6.0 percentage points (pp) lower than that estimated using measured BMI for men and women, respectively. Corrected BMI (based on models containing all significant predictors of misreporting of height and weight) lowered underestimation of obesity to 0.8pp in both sexes and improved the sensitivity of obesity over self-reported BMI by 15.0pp for men and 12.2pp for women. Results based on simpler models using age alone as a predictor of misreporting were similar.
CONCLUSIONS: Compared with self-reported data, applying prediction equations improved the accuracy of obesity prevalence estimates and increased sensitivity of being classified as obese. Including additional sociodemographic variables did not improve obesity classification enough to justify the added complexity of including them in prediction equations.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
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| Enthalten in: |
BMJ open - 13(2023), 1 vom: 13. Jan., Seite e061809 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Scholes, Shaun [VerfasserIn] |
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| Links: |
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| Themen: |
EPIDEMIOLOGY |
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| Anmerkungen: |
Date Completed 17.01.2023 Date Revised 31.01.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1136/bmjopen-2022-061809 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 0 | |a Does the use of prediction equations to correct self-reported height and weight improve obesity prevalence estimates? A pooled cross-sectional analysis of Health Survey for England data |
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| 500 | |a Date Revised 31.01.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. | ||
| 520 | |a OBJECTIVE: Adults typically overestimate height and underestimate weight compared with directly measured values, and such misreporting varies by sociodemographic and health-related factors. Using self-reported and interviewer-measured height and weight, collected from the same participants, we aimed to develop a set of prediction equations to correct bias in self-reported height and weight and assess whether this adjustment improved the accuracy of obesity prevalence estimates relative to those based only on self-report | ||
| 520 | |a DESIGN: Population-based cross-sectional study | ||
| 520 | |a PARTICIPANTS: 38 940 participants aged 16+ (Health Survey for England 2011-2016) with non-missing self-reported and interviewer-measured height and weight | ||
| 520 | |a MAIN OUTCOME MEASURES: Comparisons between self-reported, interviewer-measured (gold standard) and corrected (based on prediction equations) body mass index (BMI: kg/m2) including (1) difference between means and obesity prevalence and (2) measures of agreement for BMI classification | ||
| 520 | |a RESULTS: On average, men overestimated height more than women (1.6 cm and 1.0 cm, respectively; p<0.001), while women underestimated weight more than men (2.1 kg and 1.5 kg, respectively; p<0.001). Underestimation of BMI was slightly larger for women than for men (1.1 kg/m2 and 1.0 kg/m2, respectively; p<0.001). Obesity prevalence based on BMI from self-report was 6.8 and 6.0 percentage points (pp) lower than that estimated using measured BMI for men and women, respectively. Corrected BMI (based on models containing all significant predictors of misreporting of height and weight) lowered underestimation of obesity to 0.8pp in both sexes and improved the sensitivity of obesity over self-reported BMI by 15.0pp for men and 12.2pp for women. Results based on simpler models using age alone as a predictor of misreporting were similar | ||
| 520 | |a CONCLUSIONS: Compared with self-reported data, applying prediction equations improved the accuracy of obesity prevalence estimates and increased sensitivity of being classified as obese. Including additional sociodemographic variables did not improve obesity classification enough to justify the added complexity of including them in prediction equations | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a PUBLIC HEALTH | |
| 650 | 4 | |a STATISTICS & RESEARCH METHODS | |
| 700 | 1 | |a Ng Fat, Linda |e verfasserin |4 aut | |
| 700 | 1 | |a Moody, Alison |e verfasserin |4 aut | |
| 700 | 1 | |a Mindell, Jennifer S |e verfasserin |4 aut | |
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